In some data-intensive applications, large volumes of data may be accessed from memory. The accessing of large amounts data stored in memory may be a bottleneck in a process, and in some instances may be limited by the bandwidth between a memory device and a computational unit.
For example, deep neural networks (DNNs) may include many data processing layers each consisting of numerous nodes. Training of a DNN may involve applying node weights stored in memory to potentially hundreds of thousands of training data sets. For these and other data applications, it would be beneficial to utilize data values stored in memory efficiently.
These drawings depict example embodiments for illustrative purposes, and variations, alternative configurations, alternative components and modifications may be made to these example embodiments.